Image matching techniques may be used in a variety of applications such as control of industrial processes, tracking, detecting events, organizing or retrieving image data, and object or place recognition.
The effectiveness of object recognition may depend on the image matching algorithm that is used by an object recognition process. An image matching algorithm may utilize a computed parameter such as a descriptor of a digital image for use by the recognition process. A descriptor of a digital image, for example, may refer to characteristics of or features found in an image. Descriptors may also be local and need not describe an entire image or an object in an image. Descriptors for different images may be compared using a variety of distance metrics to find matching regions in other images.
Some objects and simple structures, such as edges or crosses, are common features in local image patches and are likely to produce false matches when using local descriptors for object recognition. Such features often lack distinctiveness. Some methods for detecting non-distinctive descriptors have been attempted. However, many attempts fail to achieve good performance due to the difficulty in collecting a labeled dataset for training. Such attempts may also fail due to the large variety of local structures that can be shared by different objects.
Systems and methods for visual object recognition are needed that reduce false matches and improve performance of an image matching process as compared to present methods.